Maximum Likelihood Slow Frequency-Selective Fading Channel Estimation Using Frequency Domain approach

This paper addresses the channel estimation problem for slow frequency-selective fading channels using training sequences and the maximum likelihood (ML) approach. In the literature people usually assume a symbol period spaced delay-tapped-line model and additive white Gaussian noise (AWGN). Due to the pre-filtering in the receiver front end, if the sampling rate is larger than one sample per symbol or sampling epoch is unknown (Le., the timing information is unavailable), the AWGN model is not valid anymore. A more general ML channel estimation method using the discrete Fourier transform (DFT) is derived for colored Gaussian noise and over-sampling. A similar idea can be adopted to derive the ML joint carrier phase and timing offsets estimation algorithm.